Moment Quantization for Video Temporal Grounding

2025-04-03

AI Summary

To address the weak discriminability of continuous features in temporal video grounding—particularly the difficulty in distinguishing relevant from irrelevant moments—this paper proposes a discrete moment quantization framework. Methodologically, it introduces (1) a learnable discrete codebook coupled with a lossless, clustering-based soft matching mechanism, replacing conventional hard quantization; (2) prior-guided codebook initialization and joint projection to enhance codebook representational quality; and (3) a plug-and-play architectural design for seamless integration. The approach achieves significant improvements over state-of-the-art methods on six mainstream benchmarks. Qualitative analysis demonstrates its ability to effectively aggregate semantically relevant segments while suppressing irrelevant temporal instances, thereby substantially enhancing discriminative capability for temporal localization.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Video temporal grounding is a critical video understanding task, which aims to localize moments relevant to a language description. The challenge of this task lies in distinguishing relevant and irrelevant moments. Previous methods focused on learning continuous features exhibit weak differentiation between foreground and background features. In this paper, we propose a novel Moment-Quantization based Video Temporal Grounding method (MQVTG), which quantizes the input video into various discrete vectors to enhance the discrimination between relevant and irrelevant moments. Specifically, MQVTG maintains a learnable moment codebook, where each video moment matches a codeword. Considering the visual diversity, i.e., various visual expressions for the same moment, MQVTG treats moment-codeword matching as a clustering process without using discrete vectors, avoiding the loss of useful information from direct hard quantization. Additionally, we employ effective prior-initialization and joint-projection strategies to enhance the maintained moment codebook. With its simple implementation, the proposed method can be integrated into existing temporal grounding models as a plug-and-play component. Extensive experiments on six popular benchmarks demonstrate the effectiveness and generalizability of MQVTG, significantly outperforming state-of-the-art methods. Further qualitative analysis shows that our method effectively groups relevant features and separates irrelevant ones, aligning with our goal of enhancing discrimination.

Problem

• Enhance discrimination between relevant and irrelevant video moments
• Quantize video into discrete vectors for better temporal grounding
• Improve moment-codeword matching without losing useful information

Innovation

• Quantizes video into discrete vectors for better discrimination
• Uses learnable moment codebook for moment-codeword matching
• Employs prior-initialization and joint-projection strategies